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From data-architecture
Choose the right database technology for specific workloads. Evaluate relational, NoSQL, data warehouses, and search engines. Use when selecting storage systems for new features or optimizing existing ones.
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Choose optimal database technology by analyzing workload characteristics, consistency requirements, and operational complexity.
Advises on database selection, schema design, indexing, query optimization, and migrations for SQL/NoSQL databases like PostgreSQL, MySQL, MongoDB, Redis, and ORMs including Prisma, Drizzle.
Designs scalable database architectures from scratch, selects technologies like PostgreSQL or DynamoDB, models schemas, indexes, and plans migrations or re-architecting.
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Choose optimal database technology by analyzing workload characteristics, consistency requirements, and operational complexity.
You are helping select storage systems for specific data requirements. Analyze access patterns, consistency needs, data volume, and operational constraints. Read any existing schemas or architectural context provided.
Based on Martin Kleppmann's Designing Data-Intensive Applications:
Catalog Data Requirements: List primary access patterns (read-heavy? write-heavy? both?), data volume growth rate, consistency needs (strong/eventual?), query types (transactional vs analytical), and operational SLAs.
Match Workload to Storage Type: Transactional workload with complex queries → PostgreSQL. Document-oriented objects → MongoDB. Time-series metrics → Prometheus or InfluxDB. Full-text search → Elasticsearch. Analytical queries on massive datasets → BigQuery/Redshift.
Evaluate Consistency vs Availability: Need ACID guarantees across distributed systems? Relational with replication. Can tolerate eventual consistency? DynamoDB/Cassandra. Determine RTO/RPO requirements.
Project Operational Burden: Managed services (RDS, DynamoDB, BigQuery) reduce ops overhead. Self-hosted databases require backup, monitoring, scaling, tuning expertise.
Cost Analysis: Storage cost (per GB), compute cost (per hour or per query), backup/replication costs. Cross-reference with projected data growth and query volume.